Enhancing field service efficiency by improving demand predictability for a Global Hi-Tech OEM

About the client

A leading ATM manufacturer & self-service solution provider operating across the US

Problems:

• The customer was operating in multiple US geographies and serving over 100K order requests per month nationally. However, the customer lacked a data-driven workforce planning and allocation process which led to idle times, impacted service order fulfillment, which in turn, caused revenue leakage and diminished brand image

Solution:

• Innover developed an advanced Machine Learning driven service order demand forecasting model by region in just 6 weeks by leveraging past 24 months historical order data
• Analyzed demand seasonality and order completion time for each task category, peak hours; and identified statistically significant time-slots for future planning
• Created a demand planning and capacity allocation simulator dashboard
• Delivered field force capacity (hours) allocation recommendations, in terms of task category and time slot